# Meta Llama 3 8B Instruct Reference vs Qwen3.5 9B FP8

On provider list prices, Meta Llama 3 8B Instruct Reference costs $0.20 per million input tokens against $0.17 for Qwen3.5 9B FP8: effectively level. Output is $0.20 against $0.25 (1.3x). On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Meta Llama 3 8B Instruct Reference | Qwen3.5 9B FP8 |
| --- | --- | --- |
| Lab | Meta | Qwen |
| Access | Open weights | Open weights |
| Context window | 8K tokens | 256K tokens |
| List price, input | $0.20 / M tokens | $0.17 / M tokens |
| List price, output | $0.20 / M tokens | $0.25 / M tokens |
| Cached input | n/a | n/a |
| License | Llama community | Not listed |
| Fine-tunable | Yes | Yes |

Specifications and provider list prices from the Allocate catalog, checked 2026-07-08. Billed price is list plus the 7% transaction fee.

## What the numbers say

Take 1,000,000 requests a month at 1,200 input and 350 output tokens each. That workload costs $291.50 a month on Qwen3.5 9B FP8 and $310 on Meta Llama 3 8B Instruct Reference at list: a gap of $18.50.

Qwen3.5 9B FP8 reads 256K tokens per request against 8K for Meta Llama 3 8B Instruct Reference, 32.0x the window. That decides which one can take whole documents without splitting them.

## Choose Meta Llama 3 8B Instruct Reference for

- Training toward a model you own

## Choose Qwen3.5 9B FP8 for

- The lower list price ($0.17 in / $0.25 out per M tokens)
- The longer context window (256K vs 8K tokens)

## Common questions

### Which is cheaper, Meta Llama 3 8B Instruct Reference or Qwen3.5 9B FP8?

Qwen3.5 9B FP8, on this workload shape. At list prices it is $0.17/$0.25 per million tokens in and out against $0.20/$0.20 for Meta Llama 3 8B Instruct Reference. Billed on Allocate: $0.18/$0.27 against $0.21/$0.21, list plus 7%.

### Which has the bigger context window?

Qwen3.5 9B FP8: 262,144 tokens (256K) against 8,192 (8K) for Meta Llama 3 8B Instruct Reference.

### Can I fine-tune Meta Llama 3 8B Instruct Reference or Qwen3.5 9B FP8?

Both publish open weights (Meta Llama 3 8B Instruct Reference: Llama community; Qwen3.5 9B FP8: Not listed), so both can be fine-tuned. On Allocate the trained weights stay inside your boundary and belong to you.

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[HTML page](https://allocate.network/compare/meta-llama-3-8b-chat-hf-vs-qwen-qwen3-5-9b) · [Meta Llama 3 8B Instruct Reference](https://allocate.network/models/meta-llama-3-8b-chat-hf.md) · [Qwen3.5 9B FP8](https://allocate.network/models/qwen-qwen3-5-9b.md) · [Machine-readable catalog](https://allocate.network/catalog.json)